熵—证券投资组合风险的一种新的度量方法

本文在研究马科维茨 ( Markowitz)证券投资组合模型的基础上 ,分析了该模型用方差度量风险的缺陷 ,进而提出用熵作为风险的度量方法 ,改进马科维茨 ( Markowitz)证券投资组合模型 ,并建立新的证券投资组合优化模型     

均值-叉熵证券投资组合优化模型

在研究马科维茨(Markowitz)证券投资组合模型的基础上,分析了该模型用方差度量风险的缺陷,进而提出用叉熵作为风险的度量方法,建立了均值-叉熵的投资组合优化模型.该模型计算简便,更易被一般投资人所使用.     

目标规划法在证券组合投资中的应用

证券投资是目前我国经济中的一大热点。本以Markowitz证券组合投资理论为基础，运用目标规划的方法建立一种新的证券组合投资决策模型。在本模型中综合考虑了证券组合的收益，风险，交易费用等因素，对投资选择有效证券组合有一定的实用价值。     

不确定条件下具有容差项的Markowitz证券组合投资模型的最优化解法

在分析证券市场中证券组合投资不确定性质的基础上,通过对Markowitz模型中证券期望收益与方差引入容差项来度量证券市场的不确定性,建立了不确定条件下具有容差项的Markowitz证券组合投资模型;分类讨论了容差的上界与下界所对应的两类有效组合前沿,得到了不确定条件下的证券组合投资模型的最优化解法及相关定理;最后给出了一个具体的数值实例．     

静态证券组合理论与CAPM—金融理论及其应用（Ⅱ）

1 前言在静态证券组合理论中,我们假定投资者是依据收益率的均值和方差来决定证券组合的.亦即使均值增大、方差减小的投资.这个资产选择的均值方差模型,从大约40年前的Markowitz(1952)以来,大量地被运用于金融中,其理由,并不在于其假定的一般性,而是在于其优良的解析性.而且,如果在均值方差模型上附加上供需均衡条件,就可以导出被称为资产期望收益率的资本性资产评价模型(Capital Asset Pricing Model(CAPM)),这在现今都是一个最重要的资产评价模型. 本文将简洁地说明静态证券组合理论,导出关于均衡中的资产期望收益率的结果.另外,作为CAPM的应用.介绍资本预算法及其对证券投资的应用.本文的构成:第2节是静态证券组合理论;第3节是为引入CAPM的前沿证券组合的数理;第4节是CAPM;第5节是CAPM的应用;最后是结论.     

具有交易成本的证券组合投资决策研究

本文利用均值－方差模型，分析了有交易成本的证券投资组合的决策问题，给出了风险资产和无风险资产的最优投资比例与交易成本关系的一个有意义的结论。     

不允许卖空的组合证券投资策略的确定

以Ｍａｒｋｏｗｉｔｚ的均值——方差模型的理论为基础，研究了组合证券投资策略确定过程中的两个主要问题：投资对象的选择方法和风险选择方法     

摩擦市场的均值-离差证券组合选择模型

本文给出了基于历史收益率数据的均值 -平均绝对离差型证券组合投资模型 .该模型采用收益的平均绝对离差作为风险的尺度 ,可以通过求解线性规划来获的摩擦市场 (如具有税收和交易费 )最优投资组合 ,避免了均值 -方差模型求解二次规划的复杂性 .     

限制投资下界的风险证券有效组合模型及算法研究

本文研究了具有投资下界限制的风险证券有限组合决策问题，提出了限制投资下界的风险证券有效组合优化模型，在一定的条件下，给出了风险证券有限组合投资比例的算法及解析表示，最后进行了实际数值计算，结果说明了所给算法是有效和实用的。     

证券组合的模糊规划模型

通过结构元方法定义了一种模糊数排序准则,利用模糊约束将Markowitz投资组舍模型转化为模糊线性规划模型,并利用模糊数来描述证券的期望收益率和风险损失率,建立模糊数模糊证券投资组合模型.最后,利用定义的模糊数排序准则把模糊数规划问题转化为经典的线性规划问题,然后再对该模型进行求解,并通过算例阐述了该方法的有效性.     

Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature

Since the pioneering work of Harry Markowitz, mean–variance portfolio selection model has been widely used in both theoretical and empirical studies, which maximizes the investment return under certain risk level or minimizes the investment risk under certain return level. In this paper, we review several variations or generalizations that substantially improve the performance of Markowitz’s mean–variance model, including dynamic portfolio optimization, portfolio optimization with practical factors, robust portfolio optimization and fuzzy portfolio optimization. The review provides a useful reference to handle portfolio selection problems for both researchers and practitioners. Some summaries about the current studies and future research directions are presented at the end of this paper.     

使用MTV模型与Morkowitz证券组合选择模型的投资决策系统

利用期望和方差的秩系数法、MTV模型、Morkowitz证券组合选择模型构造了一实用的投资决策系统.     

Financial portfolio management through the goal programming model: Current state-of-the-art

Since Markowitz (1952) formulated the portfolio selection problem, many researchers have developed models aggregating simultaneously several conflicting attributes such as: the return on investment, risk and liquidity. The portfolio manager generally seeks the best combination of stocks/assets that meets his/her investment objectives. The Goal Programming (GP) model is widely applied to finance and portfolio management. The aim of this paper is to present the different variants of the GP model that have been applied to the financial portfolio selection problem from the 1970s to nowadays.     

Bounds on efficient outcomes for large-scale cardinality-constrained Markowitz problems

Journal of Global Optimization - When solving large-scale cardinality-constrained Markowitz mean–variance portfolio investment problems, exact solvers may be unable to derive some efficient...     

Multiple criteria linear programming model for portfolio selection

The portfolio selection problem is usually considered as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In the classical Markowitz model the risk is measured with variance, thus generating a quadratic programming model. The Markowitz model is frequently criticized as not consistent with axiomatic models of preferences for choice under risk. Models consistent with the preference axioms are based on the relation of stochastic dominance or on expected utility theory. The former is quite easy to implement for pairwise comparisons of given portfolios whereas it does not offer any computational tool to analyze the portfolio selection problem. The latter, when used for the portfolio selection problem, is restrictive in modeling preferences of investors. In this paper, a multiple criteria linear programming model of the portfolio selection problem is developed. The model is based on the preference axioms for choice under risk. Nevertheless, it allows one to employ the standard multiple criteria procedures to analyze the portfolio selection problem. It is shown that the classical mean-risk approaches resulting in linear programming models correspond to specific solution techniques applied to our multiple criteria model. This revised version was published online in June 2006 with corrections to the Cover Date.     

金融资产多分辨风险识别及投资组合策略

为有效地揭示金融市场微观结构、反映和分散金融风险,基于最大重复离散小波变换对高频数据进行了多分辨分析,利用小波方差和小波协方差给出多分辨Beta系数的计算方法,讨论了高频金融资产不同时间尺度下的风险组成。针对收益与风险的多分辨特征,提出多分辨投资组合策略,改进了Markowitz的静态投资组合方法。实证研究表明,同一金融资产在不同时间尺度下的收益与风险存在多分辨特征;CAPM的表现也具有多分辨特征;而多分辨的投资组合策略则将不同时间尺度下的投资风险降到了最低。